A semantic trajectory data warehouse for improving nursing productivity


A Trajectory Data Warehouse is a central repository of large amount of data focusing on moving objects, which have been collected and integrated from multiple sources with spatial and temporal dimensions as the main metrics of analysis. By adding semantic-related contextual information, it is converted to a Semantic Trajectory Data Warehouse. It transforms raw trajectories to valuable information that can be utilized for decision-making purposes in ubiquitous applications. Human recourses management is a domain that may benefit significantly from semantic trajectory data warehouses. In particular, employees working shifts can be considered as trajectories. In this work, standard data warehousing tools are used to store data about nursing personnel shifts as trajectories of moving persons. The conceptual and logical modelling of the semantic trajectory data warehouse is developed. The objective is the observation, management and scheduling of nurses’ shifts data by the computation of OLAP operations over them. A prototype implementation has also been realized to illustrate the functionality of the proposed model. The produced results prove the efficiency in improving nursing productivity.

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  1. 1.

    Spaccapietra S, Parent C, Damiani ML, de Macedo JA, Porto F, Vangenot C. A conceptual view on trajectories. Data Knowl Eng. 2008;65(1):126–46. https://doi.org/10.1016/j.datak.2007.10.008.

    Article  Google Scholar 

  2. 2.

    Orlando S, Orsini R, Raffaetà A, Roncato A, Silvestri C. Trajectory data warehouses: design and implementation issues. J Comput Sci Eng. 2007;1(2):240–61. https://doi.org/10.5626/JCSE.2007.1.2.211.

    Article  Google Scholar 

  3. 3.

    Pelekis N, Raffaetà A, Damiani ML, Vangenot C, Marketos G, Frentzos E, Ntoutsi I, Theodoridis Y. Towards trajectory data warehouses. In: Giannotti F, Pedreschi D, editors. Mobility, data mining and privacy: geographic knowledge discovery. Heidelberg: Springer; 2008. p. 189–211.

    Google Scholar 

  4. 4.

    Alsahfi T, Almotairi M, Elmasri R. A survey on trajectory data warehouse. Spat Inf Res. 2020;28:53–66. https://doi.org/10.1007/s41324-019-00269-x.

    Article  Google Scholar 

  5. 5.

    de Almeida DR, de Souza BC, de Andrade FG, Soares A. A survey on big data for trajectory analytics. ISPRS Int J Geo-Inf. 2020;88:1–24. https://doi.org/10.3390/ijgi9020088.

    Article  Google Scholar 

  6. 6.

    Braz FJ. Trajectory data warehouse: proposal of design and application to exploit data. In: 9th Brazilian symposium on geoinformatics, Campos do Jordao, Sao Paulo, Brazil; 2007; pp 61–72.

  7. 7.

    Raffaetà A, Leonardi L, Marketos G, Andrienko G, Andrienko NV, Frentzos E, Giatrakos N, Orlando S, Pelekis N, Roncato A, Silvestri C. Visual mobility analysis using T-Warehouse. Int J Data Wareh Min. 2011;7:1–23. https://doi.org/10.4018/jdwm.

    Article  Google Scholar 

  8. 8.

    Campora S, de Macedo JAF, Spinsanti L. St-Toolkit: a framework for trajectory data warehousing. In: Geertman S, Reinhardt W, Toppen F (eds.) 14th AGILE international conference on geographic information science, Utrecht, Netherlands; 2011; pp 1–12.

  9. 9.

    Wagner R, de Macêdo JAF, Raffaetà A, Renso C, Roncato, A., Trasarti, R. MobWarehouse: a semantic approach for mobility analysis with a trajectory data warehouse. In: Parsons J., Chiu D. (eds.) Advances in conceptual modeling. ER Workshops SeCoGIS 2013. Lecture Notes in Computer Science, vol 8697. Springer, Cham; 2014; pp 127–136.

  10. 10.

    Da Silva MCT, Times VC, de Macêdo JAF, Renso C. SWOT: a conceptual data warehouse model for semantic trajectories. In: Proceedings of the ACM 18th international workshop on data warehousing and OLAP (DOLAP), pp 11–14 (2015). https://doi.org/10.1145/2811222.2811232

  11. 11.

    Kwakye MM. Semantic data warehouse modelling for trajectories. Technical Report, University of Calgary Science Research & Publications, (2017). https://doi.org/10.11575/PRISM/31331

  12. 12.

    Nardini FM, Orlando S, Perego R, Raffaetà A, Renso C, Silvestri C. Analysing trajectories of mobile users: from data warehouses to recommender systems. In: Flesca S, Greco S, Masciari E, Saccà D, editors. A comprehensive guide through the Italian Database Research over the last 25 years. Studies in big data, vol. 31. Cham: Springer; 2018. p. 407–421.

    Google Scholar 

  13. 13.

    Vaisman A, Zimanyi E. Mobility data warehouses. ISPRS Int J Geo-Inf. 2019;8(4):1–22. https://doi.org/10.3390/ijgi8040170.

    Article  Google Scholar 

  14. 14.

    Manaa M, Sakouhi T, Akaichi J. A trajectory ontology design pattern for semantic trajectory data warehouses: behavior analysis and animal tracking case studies. In: Taniar D, Rahayu W, editors. Emerging perspectives in big data warehousing. Hershey: IGI Global; 2019. p. 83–104.

    Google Scholar 

  15. 15.

    Mello RD, Bogorny V, Alvares LO, Santana LHZ, Ferrero CA, Frozza AA, Schreiner GA, Renso C. MASTER: a multiple aspect view on trajectories. Trans GIS. 2019;23(4):805–22. https://doi.org/10.1111/tgis.12526.

    Article  Google Scholar 

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Correspondence to Georgia Garani.

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Garani, G., Adam, G.K. A semantic trajectory data warehouse for improving nursing productivity. Health Inf Sci Syst 8, 25 (2020). https://doi.org/10.1007/s13755-020-00117-5

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  • Data warehouse
  • Conceptual modelling
  • Logical modelling
  • Moving object
  • Trajectory